Interactive pricing, the subset of dynamic pricing where buyers and sellers enter a computer mediated price-negotiation process, has stimulated academic interest ever since the introduction of Internet-related B2C and B2B applications. However, this has not yet led to the widespread use of standardized interactive pricing mechanisms within industrial applications. A recent study suggests that applicants expect the integration of interactive pricing mechanisms into existing IT infrastructure to be very costly due to high customization efforts. The standardization of interactive pricing should thus be a first step towards enabling a wider use of these mechanisms. Building on the classification of the range of dynamic pricing methods, we analyze existing business standards that should be capable of describing interactive pricing mechanisms. Our analysis reveals the shortcomings of recent business standards which therefore require the development of an enhanced model for interactive pricing applications. Addressing this issue we propose a model that integrates a price communication language with a process description format for the customization of interactive pricing mechanisms. The paper concludes with three case studies illustrating the use of our model.

In this article we present an agent-based simulation environment
for task scheduling in a grid-like computer system. The scheduler allows to simultaneously allocate resources such as CPU time, communication bandwidth, volatile and non-volatile memory by employing a combinatorial
resource allocation mechanism. The allocation is performed by an iterative combinatorial auction in which proxy-bidding agents try to
acquire their desired resource allocation profiles with respect to limited monetary budget endowments. To achieve an efficient allocation process, the auctioneer provides resource price information to the bidders. We use a pricing mechanism based on shadow prices in a closed loop system in which the agents use monetary units rewarded for the resources they provide to the system for the acquisition of complementary capacity. Our objective is to identify optimal bidding strategies in the multi-agent setting with respect to varying preferences in terms of resource quantity and waiting time for the resources. Based on a utility function we characterize two types of agents: a quantity maximizing agent with a low preference for fast bid acceptance and an impatient bidding agent with a high valuation of a fast access to the resources. By evaluating different strategies with varying initial bid pricing and price increments, it turns out that for quantity maximizing agents patience and low initial bids pay off, whereas impatient agents should avoid high initial bid prices.

This paper presents an agent-based simulation environment for task scheduling in a distributed computer systems (grid). Resource allocation is performed by an iterative combinatorial auction in which proxy-bidding agents try to acquire their desired resource allocation profiles.
To achieve an efficient bidding process, the auctioneer provides the
bidding agents with approximated shadow prices from a linear programming
formulation. The objective of this paper is to identify optimal bidding strategies in multi-agent settings with respect to varying preferences in terms of resource quantity and waiting time until bid acceptance. On the basis of a utility function we characterize two types of agents: a quantity maximizing agent with a low preference for fast bid acceptance and
an impatient bidding agent with a high valuation of fast allocation of the requested resources. Bidding strategies with varying initial bid pricing and different price increments are evaluated. Quantity maximizing agents should submit initial bids with low and slowly increasing prices, whereas impatient agents should start slightly below market prices and avoid ‘overbidding’.

The paper presents an agent-based simulation environment for task scheduling in distributed computer systems (grid). The scheduler enables the simultaneous allocation of bundles comprising resources such as CPU time, communication bandwidth, volatile, and non-volatile memory. The resources are allocated by an iterative combinatorial auction with proxybidding
agents trying to acquire their desired resource allocation profiles. In order to support an efficient bidding process, the auctioneer provides resource price information to the bidding agents. Due to the complementarities and substitutionalities of the bid bundles in the proposed setting, the calculation of these prices is computationally expensive. This article proposes two different price approximation mechanisms: one scarcity basedscheme and a second approach using shadow prices based on the dual formulation of the relaxed linear program of the allocation problem. The stability of both allocation mechanisms is compared in the context of a closed grid system (economy) where agents buy and sell production capacity. The objective of each agent
is to acquire complementary resource capacity to increase his productivity. In order to increase their budgets, the agents offer dispensable resource capacities. The system’s pricing and the´agents’ bidding behavior is evaluated based on both measures in situations of gradually increasing resource failure to test the stability of the allocation mechanism.

This paper presents an agent-based simulation environment for task scheduling in a distributed computer systems (grid). The scheduler
allows for the simultaneous allocation of resources such as CPU time, communication bandwidth, volatile and non-volatile memory while employing a combinatorial resource allocation mechanism. The resource allocation is performed by an iterative combinatorial auction in which proxy-bidding agents try to acquire their desired resource allocation profiles with respect to limited monetary budget endowments. To achieve an efficient bidding process, the auctioneer provides resource price information to the bidding agents. We use a pricing mechanism that approximates shadow prices from a linear programming formulation in a closed
loop grid system in which the agents use monetary units rewarded for the resources they provide to the system for the acquisition of complementary
capacity.
The objective of this paper is to identify optimal bidding strategies in
multi-agent settings with respect to varying preferences in terms of resource
quantity and waiting time until bid acceptance. Based on a utility
function we characterize two types of agents: a quantity maximizing agent
with a low preference for fast bid acceptance and an impatient bidding
agent with a high valuation of fast allocation of the requested resources.
Bidding strategies with varying initial bid pricing and different price increments
are evaluated. For quantity maximizing agents patience and low
initial bids pay off, whereas impatient agents should avoid “overbidding”.

Agent-based Computational Economics has proved to be a promising approach to explore diffusion dynamics in networks. Introducing a simulation framework, the diffusion of communication standards in different supply networks is analyzed. Agents’ decisions hereby depend on potential cost reduction, pressure from members of their communication network
and implementation costs of the standards. Besides focusing on process specific market power distributions, the impact of relationship stability and process inter-connectivity are analyzed
as determinants of the diffusion of communication standards within different supply network topologies. In this context two real world scenarios, the automotive and the publishing industry, are used as examples for different networks. The simulation results support the thesis that increasing relationship dynamics and process inter-connectivity lead to decreasing competition of
communication standards with similar functionality. In certain settings the existence of local communication clusters along the value chain is observed, enabling the organizations within these clusters to preserve their globally inferior standardization decision. Finally, the results are compared with empirical data from the automotive and publishing industry, showing high evidence of the simulation results.

In long-term, recurring contractual relationships, which are common in the B2B-arena, reputation and trust play a crucial role. This analysis investigates the joint impact of reputation and price-based ranking of suppliers on the material flow in the supply chain. Positive reputation proves to be a key factor in reaching dominating market positions, which illustrates the importance of building brand awareness in all stages of a supply chain. Through our simula-tion, it will be observed that the ranking of suppliers by reputation-based choice has a stabilizing effect on the material flow in the supply chain. A strong reputation component in the individual choice stimulates the formation of monopolies, while the discount of reputation imposes a countertendency on this effect. The Bullwhip Effect, another phenomenon that carries a countertendency to the reputation-based monopoly effect, is observed to be even stronger for members of tiers with a high fluctuation of order rates.

The contribution of this paper is the design and a first evaluation of auction-based protocols for economic scheduling in a supply chain environment. For this purpose we designed autonomous agents that bid for processing time on required
production resources. Each production resource is associated with an auctioneer that allocates the requested time slots according to
a yield maximizing optimization strategy. In a first approach the
auction is performed as a sealed-bid one-shot scheduling auction, where agents bid competitively for production capacities within a
predefined time frame, i.e. they specify a release and due-date for
the execution of the task (time windows). We test the allocation
quality of a genetic algorithm-based solution mechanism in terms of utilization ratio and accepted bid prices using the combinatorial auction test suite problem generator to generate benchmark instances. Due to the fact that this mechanism has
some shortcomings such as lacking proofness to strategic bidding
behavior and a poor transparency of price formation with respect to the single time slots, we present an extension of our model using an open outcry ascending scheduling clock auction with increasing prices over several rounds, where the pricing process and the impact of strategic bidding can be studied more detailed.

This paper presents a decentralized negotiation protocol for cooperative economic scheduling in a supply chain
environment. For this purpose we designed autonomous agents, which maximize their profits by optimizing their local schedule and offer side payments to compensate other agents for lost profit or extra expense if a cumulative profit is achievable.
To further increase their income the agents have to apply a randomized local search heuristic to prevent the negotiation from stopping in locally optimal contracts. We show that the welfare
could be increased by using a Simulated Annealing like search strategy. Unfortunately, a naive application of this strategy makes the agents vulnerable to exploitation by untruthful partners. We develop and test a straightforward mechanism based on trust
accounts to protect the agents against systematic exploitation. This “Trusted” Simulated Annealing mechanism assures truthful revelation of the individual opportunity cost situation, as a basis
for the calculation of side payments.

The maturity of technical foundations for multi-agent systems and the support by development tools, infra-structure services, and a number of development methodologies leads to an increasing number of existing multi-agent systems. A more and more networked environment drives the demand for coupling these het-erogeneous systems to large multi-multi-agent systems. Unfortunately, the design and implementation steps necessary in this context are currently not supported by established development methodologies; conven-tional approaches mainly focus on isolated multi-agent systems. In this paper, we present an approach for the integration of heterogeneous multi-agent systems. The Agent.Enterprise system is a coupled multi-multi-agent system that has been designed and tested in the manufacturing logistics domain.

In: Special Track on Agent Technology in Business Applications (ATeBa04) at Multi-Conference on Business Information Systems (MKWI2004); Invited Paper at 1st Int. WS on Applied Artificial Intelligence and Logistics (at KI2004)

Category: Proceedings

Abstract

This paper presents the development of the Agent.Enterprise system, which is built out of five Multi-Agent Systems from the manufacturing logistics domain. Consequently, the development process has to take the distributed structure of the involved projects into account. The maturity of the technical foundations for Multi-Agent Systems and the support by development tools, infrastructure services and development methods leads to an increasing number of existing multi-agent systems and entails the need to couple them into large multi-multi-agent systems. The Agent.Enterprise development process combines aspects from established agent-oriented development with new concepts designed to interlink multi-agent systems. The structure of the coupled Multi-Agent Systems is designed to inherently meet the requirements of distributed supply chains where the information for an integrated production planning and control is not available within the whole supply chain. This functionality is an integral part of the Agent.Enterprise System. As a consequence, the system is able to handle severe disturbances at supplier sites while dealing with highly customized and complex products.

The paper presents a decentralized supply chain management approach based
on reinforcement learning. Our supply chain scenario consists of loosely coupled yield optimizing scheduling agents trying to learn an optimal acceptance strategy for the offered
jobs. The optimizer constructs a mandatory schedule by inserting the requested jobs, which arrive stochastically from the customers, gradually into a production queue if the job yields a sufcient return. To reduce complexity the agents are divided into three components.
A supply chain interface, classifying job orders, a reinforcement learning algorithm component, which makes the acceptance decision and a deterministic scheduling component, which processes the jobs and generates a preliminary state space compression. The reinforcement learning algorithm accepts offers according to their delivery due date, the job price, the timeout penalty cost, and the information provided by the scheduling component.
The tasks are finally executed on the suppliers machine following the queue's schedule. In a performance comparison of our yield optimizing agent it turns out that the reinforcement learning solution outperforms the simple acceptance heuristic for all training states.

This paper presents and compares three heuristics for the combinatorial auction problem. Besides a simple greedy (SG) mechanism, two metaheuristics, a simulated annealing (SA), and a genetic algorithm (GA) approach are developed which use the combinatorial auction process to find an allocation with maximal revenue for the auctioneer. The performance of these three heuristics is evaluated in the context of a price controlled resource allocation process designed for the control and provision of distributed information services. Comparing the SG and SA method shows that depending on the problem structure the performance of the SA is up to 20% higher than the performance of the simple greedy allocation method. The proposed GA approach, using a random key encoding, results in a further improvement of the solution quality.
Although the metaheuristic approaches result in higher search performance, the computational effort in terms of used CPU time is higher in comparison to the simple greedy mechanism. However, the absolute overall computation time is low enough to enable real-time execution in the considered IS application domain.

Multi-agent systems (MAS) offer new perspectives compared to conventional, centrally organised architectures in the scope of supply chain management (SCM). Their structure inherently meets the requirements of decentralised supply chains, whereas conventional SCM systems are often restricted in terms of dynamic behaviour, handling severe disturbances at supplier sites as well as dealing with highly customised or complex products. Since necessary data are not available within the whole supply chain, an integrated approach for production planning and control taking into account all the partners involved is not feasible. In this paper a MAS architecture integrating various intelligent agents systems on the basis of different agent platforms is presented to address the problem.

In long-term, recurring contractual relationships, which are common in the B2B-area, reputation and trust play an outstanding role. The impact of reputation and price-based assessment of suppliers on the material flow in the supply chain will be investigated in this analysis. Positive reputation proves to be a key factor to reach a market dominating position. We observed in our simulation, that the assessment of suppliers towards a reputation-based choice has a positive effect on supply chain stabilitiy. In the worst case, a strong reputationbased choice leads to the formation of monopolies. The Bullwip-Effect, that could be observed as a second phenomenon in our simulation setting, represents a countertendency to the reputation-based monopoly effect. This countereffect is observed to be even stronger for members of tiers with a high fluctuation of order rates.

The dynamic allocation of resources for the supply of Information Services and Information Products (ISIP) is of increasing importance for infrastructure and service providers in growing B2B and B2C markets. Based on the FIPA oriented Multi Agent System (MAS) platform JADE we developed a model which simulates the allocation of ISIP resources by a Combinatorial Auction (CA). An Improved Greedy CA-Algorithm (IG-CAA) and a Simulated Annealing based CA-Algorithm (SA-CAA) are proposed to solve the resulting winner determination problem. Using the bid price for task processing as a control variable has turned out to be an efficient tool even in non-economic settings. Performing multiple simulation runs, we could show the superiority of the SA-CAA to the IG-SAA for a test scenario consisting of unstructured bids. However we failed to demonstrate, that the SA-CAA is capable to handle a satisfying ISIP resource allocation in distributed systems with affordable computational expense and could only recommend the IG-CAA for this purpose, due to its lower calculation requirements.

The economic impact of the growth dynamic of standards is often described from a macroeconomic point of
view, employing network effect theory and models dealing with externalities. Game-theoretic models try to depict
and predict the situation on the microeconomic side. We follow a new approach, which simulates the system’s
behavior based on the modeling of a set of individual conduction rules, and their interaction in a closed environment. Implementing such an Agent based Computational Economics approach, using a simulation environment
called SWARM, we assume the existence of three firm sizes combined with three types of standards. Each standard has an optimal fit to a firm size, which results in reduced costs according to standardization benefits, whereas other combinations lead to lower savings respectively. In addition we postulate initial standardization cost for internal restructuring measures and different scopes of communication fitted to the three firm types. Each firm can repeatedly decide to standardize in various simulation passes depending on an expected standardization benefit. As an outcome of various simulations, we observe a dominance of the communication
standard preferred by the large companies as a function of growing network density. For an increasing communication range the same behavior emerges for all firm types. The model underpins the well known concentration tendency in real worlds’ technology markets.

The focus of this paper is the design of a mechanism that helps economic agents - either autonomously or cooperatively planning - to achieve Pareto-optimal allocation of resources via a completely decentralized coordination of a logistics network. By performing simulations with the implemented protocol using a scenario from the computer industry as a benchmark, we show the feasibility and efficiency of the designed mechanism. The approach enables each agent to exploit the external effects caused by resource constraints of its supply chain contractors by adapting its production planning. Additionally, the system’s capability to reconfigure itself in case of production resource failure is increased.